How do I choose an activation function for my neural network?
Choosing an activation function depends on your specific problem. Common options include ReLU, Sigmoid, and Tanh.
What are the advantages of using ReLU?
ReLU is computationally efficient and helps mitigate the vanishing gradient problem, making it popular for deep networks.
Are there any drawbacks to using ReLU?
Yes, ReLU can suffer from the 'dying ReLU' problem where neurons can become inactive and stop learning.
When should I use Sigmoid instead?
Sigmoid is useful for binary classification problems, especially in the output layer, but it can lead to vanishing gradients.
Can I use multiple activation functions in one network?
Absolutely! It's common to use different activation functions in different layers to optimize performance.
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